Computing device facilitating prioritization of task execution within a distributed storage network (DSN)

ABSTRACT

A computing device includes an interface configured to interface and communicate with a dispersed storage network (DSN), a memory that stores operational instructions, and a processing module operably coupled to the interface and memory such that the processing module, when operable within the computing device based on the operational instructions, is configured to perform various operations. For example, the computing device generates a prioritized request that includes at least one of a task for execution or a priority level based on information stored within a storage unit (SU) of a plurality of storage units (SUs) implemented within the DSN. Note that the information corresponds to a data object that is related to a set of encoded data slices (EDSs) that are distributedly stored within the DSN. The computing device then transmits the prioritized request to the SU and receives, from the SU, a response to the prioritized request.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility Patent Application also claims prioritypursuant to 35 U.S.C. § 120 as a continuation of U.S. Utility patentapplication Ser. No. 16/693,742, entitled “PRIORITIZATION TASK EXECUTIONWITHIN A STORAGE UNIT (SU),” filed Nov. 25, 2019, scheduled to issue asU.S. Pat. No. 10,831,544 on Nov. 10, 2020, which is a continuation ofU.S. Utility patent application Ser. No. 16/143,854, entitled “CLIENTPROVIDED REQUEST PRIORITIZATION HINTS,” filed Sep. 27, 2018, now issuedas U.S. Pat. No. 10,521,300 on Dec. 31, 2019, which is a continuation ofU.S. Utility patent application Ser. No. 15/719,259, entitled “CLIENTPROVIDED REQUEST PRIORITIZATION HINTS,” filed Sep. 28, 2017, now issuedas U.S. Pat. No. 10,127,111 on Nov. 13, 2018, which claims prioritypursuant to 35 U.S.C. § 120 as a continuation-in-part (CIP) of U.S.Utility patent application Ser. No. 15/427,934, entitled “ALLOCATINGDISTRIBUTED STORAGE AND TASK EXECUTION RESOURCES,” filed Feb. 8, 2017,now issued as U.S. Pat. No. 9,813,501 on Nov. 7, 2017, which claimspriority pursuant to 35 U.S.C. § 120 as a continuation of U.S. Utilityapplication Ser. No. 13/959,006, entitled “ALLOCATING DISTRIBUTEDSTORAGE AND TASK EXECUTION RESOURCES,” filed Aug. 5, 2013, now issued asU.S. Pat. No. 9,648,087 on May 9, 2017, which claims priority pursuantto 35 U.S.C. § 119(e) to U.S. Provisional Application No. 61/711,106,entitled “PRIORITIZING TASKS IN A DISTRIBUTED STORAGE AND TASK NETWORK,”filed Oct. 8, 2012, all of which are hereby incorporated herein byreference in their entirety and made part of the present U.S. UtilityPatent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

The prior art does not provide adequate means by which operations may beperformed within storage systems including those that operate inaccordance with “cloud storage.” There exists significant room in theprior art for improvement including the manner by which operations areperformed therein.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention;

FIG. 10A is a flowchart illustrating an example of prioritizing arequest in accordance with the present invention; and

FIG. 10B is a diagram illustrating an embodiment of a method forexecution by one or more computing devices in accordance with thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 60 is shown inFIG. 6. As shown, the slice name (SN) 60 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

In some examples, note that dispersed or distributed storage network(DSN) memory includes one or more of a plurality of storage units (SUs)such as SUs 36 (e.g., that may alternatively be referred to adistributed storage and/or task network (DSTN) module that includes aplurality of distributed storage and/or task (DST) execution units 36that may be located at geographically different sites (e.g., one inChicago, one in Milwaukee, etc.). Each of the SUs (e.g., alternativelyreferred to as DST execution units in some examples) is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc.

FIG. 9 is a schematic block diagram of another embodiment of adistributed computing system in accordance with the present invention.This diagram includes a schematic block diagram of another embodiment ofa distributed computing system that includes a plurality of distributedstorage (DS) client modules 34 and storage unit (SU) 36. Each DS clientmodule 34 of the plurality of DS client module 34 may be implemented inat least one of the user device and a computing device. The systemfunctions to prioritize access requests from the plurality of DS clientmodules 34. The access request may include a dispersed or distributedstorage network (DSN). The DSN access request may include at least oneof a read request, a write request, a delete request, a list request,etc. An access request is executed in accordance with a prioritizationscheme and a response is generated based on a result of executing therequest.

The DS client module 34 generates a prioritized request and sends theprioritized request of the SU 36. The prioritized request includes oneor more of a task for execution and a desired priority level. The DSclient module 34 selects a value of the desired priority level based onone or more of a previous response corresponding to the request, arequest type of the request, the timing requirement, a priority input,and the data type associated with the request.

The SU 36 determines an execution priority level for the prioritizedrequests based on one or more of a current loading level, executionpriority levels of previously queued requests, and the desired prioritylevel of the prioritized request. The execution priority level indicatesa priority value level relative to other execution priority levels ofother prioritized requests. For example, a rejection level indicatesthat the prioritized request will not be executed. As another example, aprocess level indicates that the prioritized request will be executed inaccordance with other queued prioritized requests. The SU 36 generatesand outputs a response to the DS client module 34. The response includesthe determined execution priority level. Next, the SU 36 executes tasksassociated with the queued prioritized requests in accordance withdetermined execution priority levels. The SU 36 may generate asubsequent response that includes a result of the execution of the tasksassociated with at least one of the queued prioritized requests. Forexample, the SU 36 generates a response that includes an encoded dataslice when the prioritized request includes a request to read theencoded data slice. The method of operation of the system to prioritizerequests is discussed in greater detail with reference to FIG. 10A.

Note that the DS client modules 34 may be implemented within variousinstantiations of the computing device 12 or 16 as described herein ortheir equivalents. For example, in some implementations, such acomputing device includes an interface configured to interface andcommunicate with a dispersed or distributed storage network (DSN),memory that stores operational instructions. And a processing moduleoperably coupled to the interface and to the memory. The processingmodule, when operable within the computing device based on theoperational instructions, is configured to perform one or more functionsthat may include generation of one or more signals, processing of one ormore signals, receiving of one or more signals, transmission of one ormore signals, interpreting of one or more signals, etc. and/or any otheroperations as described herein and/or their equivalents.

In an example of operation and implementation, a computing device isconfigured to generate a prioritized request that includes a task forexecution and/or a priority level based on information stored within astorage unit (SU) of a plurality of storage units (SUs) implementedwithin a DSN. For example, the information may correspond to a dataobject is segmented into a plurality of data segments. A data segment ofthe plurality of data segments is dispersed error encoded in accordancewith dispersed error encoding parameters to produce a set of encodeddata slices (EDSs), and the set of EDSs are distributedly stored among aplurality of SUs. Note that a decode threshold number of EDSs are neededto recover the data segment. The computing device is also configured totransmit the prioritized request to the SU. Then, the computing deviceis configured to receive, from the SU, a response to the prioritizedrequest. The response is based on an execution priority level thatindicates a priority value level relative to other execution prioritylevels of other prioritized requests when the SU determines to executethe prioritized request.

In addition, in some examples, the response includes a rejection levelwhen the SU determines not to execute the prioritized request. In suchinstances, the computing device may be further configured to re-transmitthe prioritized request to the SU. Alternatively, the computing devicemay be further configured to generate another prioritized request thatincludes another priority level based on information stored within theSU of the plurality of SUs implemented within the DSN and transmit theother prioritized request to the SU.

In certain examples, the priority level is based on a previous responsecorresponding to the prioritized request, a request type of theprioritized request, a timing requirement associated of the prioritizedrequest, a priority input prioritized request, and/or a data typeassociated with the prioritized request

Also, in some implementations, the execution priority level isdetermined by the SU and is based on a current loading level of the SU,at least one execution priority level of at least one previously queuedprioritized request, and/or a priority level of the prioritized request.

In a particular implementation, the computing device is also configuredto receive, from the SU when the SU determines to execute theprioritized request, a subsequent response that includes a result ofexecution of the prioritized request as generated by the SU.

In another example of operation and implementation, the computing deviceis configured to generate a prioritized request that includes a task forexecution and/or a priority level based on information stored within aSU of the plurality of SUs implemented within the DSN. The computingdevice is configured to transmit the prioritized request to the SU.

When the SU determines to execute the prioritized request, the computingdevice is configured to receive, from the SU, a response to theprioritized request. Such a response is based on an execution prioritylevel that indicates a priority value level relative to other executionpriority levels of other prioritized requests. Also, the computingdevice is configured to receive, from the SU, a subsequent response thatincludes a result of execution of the prioritized request as generatedby the SU.

Alternatively, when the SU determines not to execute the prioritizedrequest, the computing device is configured to receive, from the SU,another response to the prioritized request that includes a rejectionlevel. the computing device is also configured to re-transmit theprioritized request to the SU. Alternatively, the computing device isconfigured to generate another prioritized request that includes anotherpriority level based on information stored within the SU of theplurality of SUs implemented within the DSN and transmit the otherprioritized request to the SU.

In some examples, note that the decode threshold number of EDSs areneeded to recover the data segment, and a read threshold number of EDSsprovides for reconstruction of the data segment. Also, a write thresholdnumber of EDSs provides for a successful transfer of the set of EDSsfrom a first at least one location in the DSN to a second at least onelocation in the DSN. The set of EDSs is of pillar width and includes apillar number of EDSs. Also, in some examples, each of the decodethreshold, the read threshold, and the write threshold is less than thepillar number. Also, in some examples, the write threshold number isgreater than or equal to the read threshold number that is greater thanor equal to the decode threshold number.

The computing device may be implemented as any of a number of differentdevices including a managing unit that is remotely located from theother computing device within the DSN and also remotely located from atleast one SU of the plurality of SUs within the DSN. In other examples,the computing device may be implemented as a SU of the plurality of SUswithin the DSN, a wireless smart phone, a laptop, a tablet, a personalcomputers (PC), a work station, or a video game device. Also, the DSNmay be implemented to include or be based on any of a number ofdifferent types of communication systems including a wirelesscommunication system, a wire lined communication system, a non-publicintranet system, a public internet system, a local area network (LAN),and/or a wide area network (WAN).

Also, note that the computing device may be located at a premises thatis remotely located from at least one SU of a plurality of SUs withinthe DSN. Also, note that the computing device may be of any of a varietyof types of devices as described herein and/or their equivalents.

FIG. 10A is a flowchart illustrating an example of prioritizing arequest in accordance with the present invention. This diagram includesa flowchart illustrating an example of prioritizing a request. Themethod 1001 begins with the step 1010 where a distributed storage (DS)client module generates a prioritized request. For example, the DSclient module generates a read slice request with a higher than averagedesired priority level value to enable recreation of an important datafile. The method 1001 continues at the step 1020 where the DS clientmodule sends the prioritized request to a storage unit (SU). The method1001 continues at the step 1030 where the SU determines an executionpriority level for the prioritized request. The method 1001 continues atthe step 1040 where the SU outputs the execution priority level to theclient module. For example, the SU generates a response that includesthe execution priority level corresponding to the prioritized request.The method 1001 continues at the step 1050 where the SU executes a taskassociated with the prioritized request in accordance with the executionpriority level and other execution priority levels associated with othertasks of other prioritized requests.

FIG. 10B is a diagram illustrating an embodiment of a method forexecution by one or more computing devices in accordance with thepresent invention. The method 1002 begins with the step 1011 bygenerating a prioritized request that includes a task for executionand/or a priority level based on information stored within a storageunit (SU) of a plurality of storage units (SUs) implemented within adispersed or distributed storage network (DSN). Note that theinformation corresponds to a data object is segmented into a pluralityof data segments. Also, note that a data segment of the plurality ofdata segments is dispersed error encoded in accordance with dispersederror encoding parameters to produce a set of encoded data slices(EDSs). The set of EDSs are distributedly stored among a plurality ofSUs, and a decode threshold number of EDSs are needed to recover thedata segment. The method 1002 continues at step 1021 by transmitting(e.g., via an interface of the computing device configured to interfaceand communicate with the DSN) the prioritized request to the SU. Themethod 1002 then operates at step 1031 by receiving (e.g., via theinterface and from the SU) a response to the prioritized request. Theresponse is based on an execution priority level that indicates apriority value level relative to other execution priority levels ofother prioritized requests when the SU determines to execute theprioritized request.

In certain variants of the method 1002, the response includes arejection level when the SU determines not to execute the prioritizedrequest. Some variants of the method 1002 operate by re-transmitting,via the interface, the prioritized request to the SU. Alternatively,other variants of the method 1002 operate by generating anotherprioritized request that includes another priority level based oninformation stored within the SU of the plurality of SUs implementedwithin the DSN and transmitting, via the interface, the otherprioritized request to the SU.

In some variants of the method 1002, the priority level is based on aprevious response corresponding to the prioritized request, a requesttype of the prioritized request, a timing requirement associated of theprioritized request, a priority input prioritized request, and/or a datatype associated with the prioritized request

In other particular variants of the method 1002, the execution prioritylevel is determined by the SU and is based on a current loading level ofthe SU, at least one execution priority level of at least one previouslyqueued prioritized request, and/or a priority level of the prioritizedrequest.

Alternative variants of the method 1002 also operate by receiving (e.g.,via the interface and from the SU and when the SU determines to executethe prioritized request) a subsequent response that includes a result ofexecution of the prioritized request as generated by the SU.

This disclosure presents, among other things, a process by which acomputing device sends requests to a storage unit (SU) may optionallyprovide hints to the SU regarding the importance (as judged by therequestor) of timely processing and response of the request by the SU.This may be represented by a priority indicator present within therequest. For example, a field which allows specification of a numberbetween 0 and 255, with higher numbers translating to higher priorities.Among other things, this enables individual clients (e.g., computingdevices) to priorities requests which are important for their purposes.Another use case is a rebuilder process may purposely de-prioritize itsrequests (listing, reading, writing) as not to intrude upon ongoinginput/output (I/O) operations, while a time-sensitive process may sendits requests with the highest priority. If the priority is below acertain threshold (e.g., under 50 on the 0-255 range), it may qualifythe request for automatic rejection if the SU is in a state where it istoo busy (e.g., its queue of outstanding requests to process issufficiently long). In this case, an error response may be returned(e.g., from the SU to the computing device) indicating the status of“SERVER BUSY”. It will then be up to the requester (e.g., the computingdevice) to decide whether to try again later, or to increase thepriority of the request and re-send it.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A computing device comprising: an interfaceconfigured to interface and communicate with a distributed storagenetwork (DSN); memory that stores operational instructions; and aprocessing module operably coupled to the interface and to the memory,wherein the processing module, when operable within the computing devicebased on the operational instructions, is configured to: generate aprioritized request related to information stored within a storage unit(SU) of a plurality of storage units (SUs) implemented within the DSN;transmit, to the SU and via the interface, the prioritized request; andreceive, from the SU and via the interface, a response to theprioritized request that includes an execution priority level that isgenerated by the SU based on one or more conditions corresponding to theSU and that indicates a priority value level of the prioritized requestin comparison to at least one other prioritized request.
 2. Thecomputing device of claim 1, wherein the priority value level is arejection level indicating that the prioritized request will not beexecuted by the SU.
 3. The computing device of claim 1, wherein thepriority value level is a process level indicating that the prioritizedrequest will be executed by the SU in accordance with queued prioritizedrequests that include the prioritized request and the at least one otherprioritized request.
 4. The computing device of claim 3, wherein the SUis further configured to: execute the queued prioritized requests inaccordance with execution priority levels of the prioritized requests ofthe queued prioritized requests that include the execution prioritylevel of the prioritized request and at least one other executionpriority level of the at least one other prioritized request.
 5. Thecomputing device of claim 1, wherein: the prioritized request includesat least one of a task for execution or a priority level based on theinformation stored within the SU of the plurality of SUs implementedwithin the DSN; and the priority level is based on at least one of aprevious response corresponding to the prioritized request, a requesttype of the prioritized request, a timing requirement associated of theprioritized request, a priority input prioritized request, or a datatype associated with the prioritized request.
 6. The computing device ofclaim 1, wherein the SU is further configured to: determine theexecution priority level based on at least one of a current loadinglevel of the SU, at least one execution priority level of at least onepreviously queued prioritized request, or the priority level of theprioritized request.
 7. The computing device of claim 1, wherein theinformation corresponds to a data object that is segmented into aplurality of data segments, wherein a data segment of the plurality ofdata segments is dispersed error encoded in accordance with dispersederror encoding parameters to produce a set of encoded data slices(EDSs), wherein the set of EDSs are distributedly stored among theplurality of SUs, and wherein a decode threshold number of EDSs areneeded to recover the data segment.
 8. The computing device of claim 7,wherein: a read threshold number of EDSs provides for reconstruction ofthe data segment; a write threshold number of EDSs provides for asuccessful transfer of the set of EDSs from a first at least onelocation in the DSN to a second at least one location in the DSN; theset of EDSs is of pillar width and includes a pillar number of EDSs;each of the decode threshold number, the read threshold number, and thewrite threshold number is less than the pillar number; and the writethreshold number is greater than or equal to the read threshold numberthat is greater than or equal to the decode threshold number.
 9. Thecomputing device of claim 1 further comprising: another SU of theplurality of SUs implemented within the DSN, a wireless smart phone, alaptop, a tablet, a personal computers (PC), a work station, or a videogame device.
 10. The computing device of claim 1, wherein the DSNincludes at least one of a wireless communication system, a wire linedcommunication system, a non-public intranet system, a public internetsystem, a local area network (LAN), or a wide area network (WAN).
 11. Amethod for execution by a computing device, the method comprising:generating a prioritized request related to information stored within astorage unit (SU) of a plurality of storage units (SUs) implementedwithin a distributed storage network (DSN); transmit, to the SU and viaan interface of the computing device that is configured to interface andcommunicate with a DSN, the prioritized request; and receive, from theSU and via the interface, a response to the prioritized request thatincludes an execution priority level that is generated by the SU basedon one or more conditions corresponding to the SU and that indicates apriority value level of the prioritized request in comparison to atleast one other prioritized request.
 12. The method of claim 11, whereinthe priority value level is a rejection level indicating that theprioritized request will not be executed by the SU.
 13. The method ofclaim 11, wherein the priority value level is a process level indicatingthat the prioritized request will be executed by the SU in accordancewith queued prioritized requests that include the prioritized requestand the at least one other prioritized request.
 14. The method of claim13 further comprising: executing, within the SU, the queued prioritizedrequests in accordance with execution priority levels of the prioritizedrequests of the queued prioritized requests that include the executionpriority level of the prioritized request and at least one otherexecution priority level of the at least one other prioritized request.15. The method of claim 11, wherein: the prioritized request includes atleast one of a task for execution or a priority level based on theinformation stored within the SU of the plurality of SUs implementedwithin the DSN; and the priority level is based on at least one of aprevious response corresponding to the prioritized request, a requesttype of the prioritized request, a timing requirement associated of theprioritized request, a priority input prioritized request, or a datatype associated with the prioritized request.
 16. The method of claim 11further comprising: determining, within the SU, the execution prioritylevel based on at least one of a current loading level of the SU, atleast one execution priority level of at least one previously queuedprioritized request, or the priority level of the prioritized request.17. The method of claim 11, wherein the information corresponds to adata object is segmented into a plurality of data segments, wherein adata segment of the plurality of data segments is dispersed errorencoded in accordance with dispersed error encoding parameters toproduce a set of encoded data slices (EDSs), wherein the set of EDSs aredistributedly stored among the plurality of SUs, and wherein a decodethreshold number of EDSs are needed to recover the data segment.
 18. Themethod of claim 17, wherein: a read threshold number of EDSs providesfor reconstruction of the data segment; a write threshold number of EDSsprovides for a successful transfer of the set of EDSs from a first atleast one location in the DSN to a second at least one location in theDSN; the set of EDSs is of pillar width and includes a pillar number ofEDSs; each of the decode threshold number, the read threshold number,and the write threshold number is less than the pillar number; and thewrite threshold number is greater than or equal to the read thresholdnumber that is greater than or equal to the decode threshold number. 19.The method of claim 11, wherein the computing device includes another SUof the plurality of SUs within the DSN, a wireless smart phone, alaptop, a tablet, a personal computers (PC), a work station, or a videogame device.
 20. The method of claim 11, wherein the DSN includes atleast one of a wireless communication system, a wire lined communicationsystem, a non-public intranet system, a public internet system, a localarea network (LAN), or a wide area network (WAN).